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Fog Computing: The New Model for the IoE

Last month, we proudly announced Connected Analytics for the Internet of Everything (IoE), easy-to-deploy software packages that bring analytics to data regardless of its location. It is a continued part of our commitment to delivering on our vision for fog computing, also called edge computing, a model that does not require the movement of data back to a centralized location for processing. If you’ve been reading my blog, you’ve seen me write about this as the concept of ‘Analytics 3.0’ or the ability to do analytics in a widely distributed manner, at the edge of the network and on streaming data. This capability is unique to Cisco and critical for deriving real-time insights in the IoE era.

To perform analytics using a traditional computing method, once data is generated it is aggregated, moved and stored into a central repository, such as a data lake or enterprise data warehouse, so it can be analyzed for insight. In the IoE, data is massive, messy, and everywhere – spanning many centralized data repositories in multiple clouds, and data warehouses. Increasingly, data is also being created in massive volume in a very distributed way…from sensors on offshore oil rigs, ships at sea, airplanes in flight, and machines on factory floors. In this new world, there are many problems that arise with the traditional method – not only is it expensive and time consuming to move all of this data to a central place, but critical data can also lose its real-time value in the process. In fact, many companies have stopped moving all of their data into a central repository and accepted the fact that data will live in multiple places.

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Analytics 3.0 creates a more appropriate model, where the path to derive insight is different by combining traditional centralized data storage and analysis with data management and analytics that happen at the edge of the network…much closer to where the huge volume of new data is being created. Analytics involves complicated statistical models and software, but the concept is simple…using software to look for patterns in data, so you can make better decisions.  It makes sense then to have this software close to where data is created, so you can find those patterns more quickly…and that’s the key concept behind Analytics 3.0. Once it’s analyzed, we can make more intelligent decisions about what data should be stored, moved or discarded. This model gives us the opportunity to get to the ‘interesting data’ quicker and also alleviates the costs of storing and moving the ‘non-interesting data.’

Analytics 3.0 is not about replacing big data analytics, cloud analytics and other centralized analytics.  Those elements are all part of Analytics 3.0, but they are not sufficient to handle the volume of massively distributed data created in the IoE, and so they must be augmented with the ability to process and analyze data closer to where it is created. By combining centralized data sources with streaming data at the edge, you will look for and find new patterns in your data. Those patterns will help you make better decisions about growing your business, optimizing your operations or better serving your customers…and that is the power of Analytics for the IoE.

 

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Could Big Data and Cloud go together?

In today’s era of increasing connectivity, data is getting generated in vast proportions.  Moreover, it is also important to be able to generate insights from it quickly and act accordingly.  Gone are the days when one would move data into a data warehouse and then extract insights from it to act at a later date.  Here are four scenarios why.

Scenario 1: Cloud and Social

If a discussion around a brand is trending positively or negatively, that organization needs to take action then and cannot wait for a future time to do so. They might want to capitalize on the positive sentiment and amplify it or perhaps take action and remedy a trending negative sentiment. Both Twitter and Facebook provide several real time analytics capabilities leveraging big data technologies that they pioneered themselves.  These analytics run within their cloud environment and provider users real time insights.

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Big Data Analytics Remains a Big Opportunity for Wealth Managers – Part I

Wealth Management firms are spending billions on IT to differentiate in the market place. Yet the question remains, can “Big Data” have a material impact on the business? Can it deliver business outcomes by reducing risk, increasing assets under management, driving profitability, client satisfaction, products per client, client and financial advisor retention, all while improving the cost/income ratio and return on equity?

These are questions that are being discussed in board rooms across the financial industry and topics that I will cover in this blog series.

In order to answer these questions we need to put the wealth client at the center and understand changing client needs and expectations around how the client wants to be served by the firm. We need to examine external factors such as the impact of game changing consumer technology and unprecedented client access to information, as well as understand how new market entrants are challenging the traditional financial advisor value proposition and business model as a new round of Robo-Advisors hit the market.

Up until recent years, banks enjoyed an account centric transactional business model. What is changing is the onset of unstructured social interaction data as smart mobile devices and mobile broadband Internet usage reach high penetration levels. Device proliferation is leading to the availability of “data exhaust” from mobile phones, tablets, automobiles, video cameras, and from sensors in buildings, streets, consumer wearables and footfall traffic counters. Correlation of such data to better attract, retain, and serve clients can create market advantage.J pagano Wealth Mgt Blog part 1

The “Big” in Big Data comes from the fact that worldwide data volume is doubling every two years with unprecedented volume, variety, and velocity. Ninety percent of the all data in the history of the world was created in the last two years (SINTEF)! The concept of Big Data is about the correlation and analysis of transaction data, social interaction data, and machine/sensor data in a way that can turn data into knowledge, knowledge into insights, and insights into actions in real-time.

So what does this all mean for wealth managers?

As a wealth manager, what impact would it have on your business if you were able to increase the understanding of your client exponentially? Actions derived from data are informed by highly personalized needs predictions that can arm wealth managers with deep insights about their clients, increase their relevance in every interaction, and directly contribute to business outcomes. Big Data can help wealth managers transform the client value proposition and re-imagine the client experience.

The new vision for financial services is that a firm must be present in the financial lives of its clients, any time, any place, on any device, and across any channel.

The firm can no longer wait for the client to come to it. It must be proactive in delivering highly relevant value-added services in real-time and anticipate client needs. The firm needs to aspire to creating a “market of one” experience for each wealth client, understand the needs of and the hierarchy within the household, and move to a client centric versus account centric go-to-market approach.

When it comes to Big Data in Wealth Management start with the foundation, put the client at the center, and define business outcomes. Focus on building capabilities around what is possible while re-imagining the client experience.

Wealth management firms can take concrete steps in the form of measurable business outcome based projects to significantly enhance the client experience. These include:

  1. Define a roadmap for wealth client data analytics maturity. This will identify gaps that can be addressed resulting in more relevant advisor-client interactions.
  2. Establish a wealth client listening system across all channels. Early detection of client behaviors can lead to the identification of issues and sales opportunities.
  3. Create a real-time single view of wealth client data with data virtualization. Substantial savings can be had by leaving disparate data in place while providing managers with a single view.
  4. Establish an analytics driven financial advisor collaboration platform. This helps create market differentiation by maximizing advisor productivity, sharing best practices daily.
  5. Deploy mobile virtual advisor video capability and establish branch analytics. This improves client experience and gives advisors more minutes per day with clients increasing cross-selling opportunities.
  6. Empower advisors with real-time client insights to drive business outcomes. This helps the advisor manage to client life events with much greater granularity and speed.

The choices that wealth management firms make around data analytics in the next two years will determine their position in the marketplace. Can Big Data help wealth managers? With a client centric and business outcomes solutions approach, the answer is an astounding YES!

I will discuss each of the above steps in more detail in my next blog. As always I welcome your suggestions, stories, and feedback!

 

 

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Cisco UCS is now Hortonworks “Operations Ready”

Cisco has been working closely with Hortonworks in delivering turnkey Big Data solutions that expedites the time to market for our joint Big Data Hadoop customers. Cisco’s industry leading UCS Integrated Infrastructure for Big Data, is designed to deliver performance at scale for a wide variety of Big Data workloads. We are working with Hortonworks to integrate Cisco UCS Director Express for Big Data with Apache Ambari, to provide a fully automated solution to deploy and manage Hadoop hardware, networking and the Hortonworks Data Platform. It is built on the solid foundation of the highly successful UCS management platform and the award winning UCS Director orchestration engine.

Today, we are excited that Cisco UCS is HDP certified and Operations Ready. The integration with Apache Ambari allows customers to now deploy and manage their Hadoop clusters in a reliable and consistent manner. The Operations Ready designation is a new certification introduced by Hortonworks to provide the additional assurance that the tool has been integrated with Apache Ambari APIs. Cisco UCS with Hortonworks delivers a fully validated solution and reduces the complexity of managing Hadoop clusters. Cisco is committed to bringing industry leading solutions for Big Data to market, in partnership with Hortonworks and other ecosystem partners.

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Connected Analytics: Capturing the Value of the Internet of Everything

Ten large oil refineries produce about 10 terabytes of data each day, which equates to the entire printed collection of the U.S. Library of Congress.

One modernized city the size of Singapore can generate about 2.5 petabytes of data every day, which translates to all U.S. academic research libraries combined.

And with more than 14 billion, data-transmitting devices connected to the Internet today, growing to 50 billion by 2020, it is little wonder that most of us are overwhelmed by this mind-boggling explosion of data.

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Turning this flood of raw data into useful information and even wisdom for better business decisions and quality of life experiences is what the Internet of Everything (IoE) is all about. This is a daunting task. According to IDC Research, just .5% of all data is used or analyzed, and online data volumes are doubling every two years from a combination of mobile devices, videos, sensors, M2M, social media, applications and much more.

Connected Analytics Portfolio

Last Thursday, however, Cisco unveiled our Connected Analytics portfolio for the Internet of Everything, a unique approach that includes software packages to bring analytics to the data, regardless of its location or whether it is in motion or at rest. This new generation of analytics tools for IoE can convert more and more data into valuable intelligence — from the inter cloud, to the data center to the network’s edge.

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